Generally, a data storage or data warehouse is computer based database utilized to store records and results pertaining to queries. The records correspond with entities, such as individuals, organizations and property etc. Each record contains identifiers of the entity, for example, name, address or account information respectively is the identifier for the entity named individual. The data storage stores results and records in rows and columns wise. Usually, one or more queries are raised from client machines for retrieving results and records for the one or more queries from the data storage. In the existing methods, different types of normalizations are carried out to organize the contents of the tables for databases and data warehouses. Sometimes, the one or more queries are of same pattern for which results and records are also of same pattern. Typically, normalization is carried out for each of the one or more queries separately. In such case, the method of normalization consumes time and bandwidth to retrieve results towards the same kinds of queries.
Also, the existing data storage is restricted to 5 normal forms. Further, the existing data storage does not perform storing of data in a way matching the human thought process. In such case, representation of data is static in nature and the nature of storage needs to be predefined at the time of creation of storage. Hence, the storage cannot be changed dynamically based on change in user demand and access pattern.
In order to overcome the problem of normalization and growing dimensions in data, a fast indexing mechanism, for e.g. Hash or factorization mechanism for e.g. Map-Reduce is implemented. However, the indexing mechanism is another way of storing the data which does not provide results based on data demand and usage pattern of different users.
Hence, there is a need for a method to cater with fast access mechanism for the same kinds of the queries and to handle increasing dimensionality of data in the data storage.